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Investigation of Biometric Identification Technology Based on biological Fingerprints and Facial Features

Huixing Li, Yan Xue, Xiancai Zeng, M.S. Tahir, L. Xing
2021 E3S Web of Conferences  
Biometric identification is largely dependent on feature extraction technology.  ...  Based on feature extraction, it will have prospected in the future biometric identification model of progress.  ...  The gradient vector-based method calculates the gradient vector of the fingerprint image at each pixel point and takes the vertical direction of the fastest gradient change direction of the center point  ... 
doi:10.1051/e3sconf/202126702035 fatcat:uiu2eni5cbce3ff26efpja26gi

Guest Editorial: Unconstrained Ear Recognition

2018 IET Biometrics  
The captured light field images contain rich spatio-angular information and are shown to be highly suitable for biometric recognition.  ...  The authors employ the dataset in transfer-learning experiments with popular CNN architectures and develop a multi-scale framework for ear feature representation that is shown to ensure considerable recognition  ... 
doi:10.1049/iet-bmt.2018.0011 fatcat:pk5aq6fhtzaejgm46mnrjn2hka

Significant Feature Based Representation for Template Protection

Deen Dayal Mohan, Nishant Sankaran, Sergey Tulyakov, Srirangaraj Setlur, Venu Govindaraju
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
The security of biometric templates is of paramount importance. Leakage of biometric information may result in loss of private data and can lead to the compromise of the biometric system.  ...  We propose a significant bit based representation which guarantees security in addition to other biometric aspects such as cancelability and reproducibility.  ...  It is also important for any biometric system to create a representation that would prevent any leak in user information if the system is compromised.  ... 
doi:10.1109/cvprw.2019.00293 dblp:conf/cvpr/MohanSTSG19 fatcat:tq6swby2nvbideoz3jefz4kvpa

A deep learning approach for person identification using ear biometrics

Ramar Ahila Priyadharshini, Selvaraj Arivazhagan, Madakannu Arun
2020 Applied intelligence (Boston)  
Similar to other biometrics such as face, iris and fingerprints, ear also has a large amount of specific and unique features that allow for person identification.  ...  Automatic person identification from ear images is an active field of research within the biometric community.  ...  Pathak Ajay Kumar, Associate Professor, The Hong Kong Polytechnic University for providing IIT Delhi Ear Database (Version1.0).  ... 
doi:10.1007/s10489-020-01995-8 pmid:34764557 pmcid:PMC7594944 fatcat:oz65x37yibfh5dggfgtdagpszi

Histogram of Gradient and Local Binary Pattern with Extreme Learning Machine Based Ear Recognition

Ahmed Kawther Hussein
2019 Journal of Southwest Jiaotong University  
In this article, an exploration of the performance of ear recognition using two features - local binary pattern and histogram of gradient - has been done using the famous dataset USTB.  ...  The achieved accuracy of the histogram of gradient based extreme learning machine was 99.86% while for local binary pattern based extreme learning machine it was 99.59%.  ...  Future work is to explore the performance of other types of features and to use 3D images for ears as input.  ... 
doi:10.35741/issn.0258-2724.54.6.31 fatcat:q7cb6mcd3fhoxnj4clnttghiqy

Dimension Reduction of Hand and Face Feature Level Fusion in Multimodal Biometric Authentication

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
The system uses either of the biometric traits for person identification with 99.98% of authentication rate.  ...  The proposed work is a multimodal biometric authentication approach with image texture feature dimension reduction of trained feature vector which leads reduction in memory size and in turn reduces the  ...  Fusion image features are extracted using deep scattering convolution network and multi-class support vector machine classifier is used for authentication [1] .  ... 
doi:10.35940/ijitee.i8924.078919 fatcat:o7y2zkeztnbbrfnut5kjmvzn3m

Guest Editorial Introduction to the Special Section on Intelligent Visual Content Analysis and Understanding

Hongliang Li, Lu Fang, Tianzhu Zhang
2020 IEEE transactions on circuits and systems for video technology (Print)  
To explore the complementary properties between the hand-crafted shallow feature representation and deep features, "Discriminative multi-view subspace feature learning for action recognition" by Sheng  ...  et al., proposes a subspace learning model for effective shallow and deep feature fusion.  ... 
doi:10.1109/tcsvt.2020.3031416 fatcat:gpwbmydqbza5lddatxcfcidwcq

A Novel Multimodal Biometrics Recognition Model Based on Stacked ELM and CCA Methods

Jucheng Yang, Wenhui Sun, Na Liu, Yarui Chen, Yuan Wang, Shujie Han
2018 Symmetry  
Second, the canonical correlation analysis method is applied to map the representation to a feature space, which is used to reconstruct the multimodal image feature representation.  ...  Third, the reconstructed features are used as the input of a classifier for supervised training and output.  ...  , we apply a two-layer stacked ELM to face image features X 1 and finger-vein image features X 2 for feature learning prior to the modal information fusion.  ... 
doi:10.3390/sym10040096 fatcat:peyzqnio75cl7ngg4lmlipm54e

Palmprint Recognition Using Deep Scattering Convolutional Network [article]

Shervin Minaee, Yao Wang
2016 arXiv   pre-print
In this paper, a powerful image representation, called scattering network/transform, is used for palmprint recognition.  ...  Many of these transform domain features are not translation or rotation invariant, and therefore a great deal of preprocessing is needed to align the images.  ...  We would also like to thank the CSIE group at NTU for providing the LIBSVM software, and biometric research group at PolyU Hong Kong for providing the palmprint dataset.  ... 
arXiv:1603.09027v1 fatcat:g2cou5jwwjgopodibogpbt34ia

Verification of Biometric Traits using Deep Learning

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
features from the input image without prior domain knowledge and classify the class of the biometric trait.  ...  A centralized database of different biometric trait images has been created using data sets of CASIA1 for iris trait, google 11K hand for palmprint trait, for face UTKFace is used.  ...  Despite of the success in recognizing five traits, deep learning approaches have been scarcely explored for other modalities.  ... 
doi:10.35940/ijitee.j1083.08810s19 fatcat:ehec3l2srvdmjbehwtgc4fse2y

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

Hong-Jun Yoon, Folami Alamudun, Kathy Hudson, Garnetta Morin-Ducote, Georgia Tourassi
2018 Human Performance in Extreme Environments  
In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing  ...  Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.  ...  In this work, we leverage the automatic feature representation advantage of deep learning for a gaze velocity-based biometric identification of medical experts.  ... 
doi:10.7771/2327-2937.1088 fatcat:jswaett745ghviijwbzquz4udy

Thermal Features for Presentation Attack Detection in Hand Biometrics [article]

Ewelina Bartuzi, Mateusz Trokielewicz
2018 arXiv   pre-print
This paper proposes a method for utilizing thermal features of the hand for the purpose of presentation attack detection (PAD) that can be employed in a hand biometrics system's pipeline.  ...  We also show that thermal images of the human hand, in addition to liveness features they carry, can also improve classification accuracy of a biometric system, when coupled with visible light images.  ...  Conclusions This paper is the first known to us work that explores thermal features of the hand for the purpose of assessing sample liveness for presentation attack detection, and employs deep convolutional  ... 
arXiv:1809.04364v1 fatcat:gbtt2tkk4bbhfayx4sh5gadv3e

An Experimental Study of Deep Convolutional Features For Iris Recognition [article]

Shervin Minaee, Amirali Abdolrashidi, Yao Wang
2017 arXiv   pre-print
In this paper, we have investigated the application of deep features extracted from VGG-Net for iris recognition.  ...  Iris is one of the popular biometrics that is widely used for identity authentication. Different features have been used to perform iris recognition in the past.  ...  Acknowledgments The authors would like to thank Vadaldi's group at Oxford University for providing the software implementation of convolutional neural network, and also CSIE group at NTU for providing  ... 
arXiv:1702.01334v1 fatcat:fbsfymoxc5cyvcvz3aqxgvyuey

Exploring Body Texture from mmW Images for Person Recognition

Ester Gonzalez-Sosa, Ruben Vera-Rodriguez, Julian Fierrez, Fernando Alonso-Fernandez, Vishal M. Patel
2019 IEEE Transactions on Biometrics Behavior and Identity Science  
After having explored shape information retrieved from mmW images for person recognition, in this work we aim to gain some insight about the potential of using mmW texture information for the same task  ...  compared to hand-crafted features on mmW faces and the entire body, and iii) hand-crafted features slightly outperform CNN features on mmW torso.  ...  Authors wish to thank also TNO for providing access to the database. The conclusion goes here.  ... 
doi:10.1109/tbiom.2019.2906367 fatcat:opgn7jeszvf6loeqylbuqyofau

Deep Learning in Information Security [article]

Stefan Thaler, Vlado Menkovski, Milan Petkovic
2018 arXiv   pre-print
Deep Learning is a sub-field of machine learning, which uses models that are composed of multiple layers.  ...  Machine learning techniques learn models from data representations to solve a task. These data representations are hand-crafted by domain experts.  ...  Exploring complementary features for iris recognition on mobile devices. In 2016 International Conference on Biometrics, ICB 2016, 2016.  ... 
arXiv:1809.04332v1 fatcat:xfb7lgrkw5cirdl3qvmg3ssnbi
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